Machine learning is becoming an essential part of a physicist’s toolkit. How should new students learn to use it? When Radha Mastandrea started her undergraduate physics program at MIT in 2015, she ...

Physics-informed machine learning bridges the gap between the high fidelity of mechanistic models and the adaptive insights of artificial intelligence. In chemical reaction network modeling, this ...

Exponential growth in big data and computing power is transforming climate science, where machine learning is playing a critical role in mapping the physics of our changing climate. "What is happening ...

A study in the Journal of Cosmology and Astroparticle Physics explores how a machine-learning strategy known as transfer learning could dramatically reduce the computational cost of searching for new ...

Coherent X-ray scattering techniques are critical for investigating the fundamental structural properties of materials at the nanoscale. While advancements have made these experiments more accessible, ...

In the world of particle physics, where scientists unravel the mysteries of the universe, artificial intelligence (AI) and machine learning (ML) are making waves with how they're increasing ...

Researchers in Sweden have developed a machine-learning approach that embeds the laws of physics directly into neural ...

For more than 20 years in experimental particle physics and astrophysics, machine learning has been accelerating the pace of science, helping scientists tackle problems of greater and greater ...

Machine learning can get a boost from quantum physics. On certain types of machine learning tasks, quantum computers have an exponential advantage over standard computation, scientists report in the ...

The Nobel Prize in physics has been awarded to John J. Hopfield of Princeton University and Geoffrey E. Hinton of the University of Toronto for discoveries and inventions that formed the building ...